Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations5986025
Missing cells54248
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 GiB
Average record size in memory689.5 B

Variable types

Numeric9
DateTime1
Text5
Categorical4

Alerts

ARREST_BORO is highly overall correlated with ARREST_PRECINCT and 1 other fieldsHigh correlation
ARREST_PRECINCT is highly overall correlated with ARREST_BOROHigh correlation
KY_CD is highly overall correlated with LAW_CAT_CDHigh correlation
LAW_CAT_CD is highly overall correlated with KY_CDHigh correlation
Latitude is highly overall correlated with Y_COORD_CDHigh correlation
Longitude is highly overall correlated with X_COORD_CDHigh correlation
X_COORD_CD is highly overall correlated with ARREST_BORO and 1 other fieldsHigh correlation
Y_COORD_CD is highly overall correlated with LatitudeHigh correlation
LAW_CAT_CD is highly imbalanced (54.3%)Imbalance
PERP_SEX is highly imbalanced (58.2%)Imbalance
Y_COORD_CD is highly skewed (γ1 = 36.55155715)Skewed
Latitude is highly skewed (γ1 = 33.47851636)Skewed
Longitude is highly skewed (γ1 = 389.6236999)Skewed
ARREST_KEY has unique valuesUnique
JURISDICTION_CODE has 5028649 (84.0%) zerosZeros

Reproduction

Analysis started2025-10-15 18:39:32.069212
Analysis finished2025-10-15 18:41:53.503256
Duration2 minutes and 21.43 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

ARREST_KEY
Real number (ℝ)

Unique 

Distinct5986025
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2194727 × 108
Minimum9926901
Maximum2.9874848 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 MiB
2025-10-15T14:41:53.535105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9926901
5-th percentile24840689
Q166256737
median90820608
Q31.7016224 × 108
95-th percentile2.7663064 × 108
Maximum2.9874848 × 108
Range2.8882158 × 108
Interquartile range (IQR)1.0390551 × 108

Descriptive statistics

Standard deviation76996740
Coefficient of variation (CV)0.63139372
Kurtosis-0.59847726
Mean1.2194727 × 108
Median Absolute Deviation (MAD)53228502
Skewness0.64987466
Sum7.2997941 × 1014
Variance5.928498 × 1015
MonotonicityNot monotonic
2025-10-15T14:41:53.566410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2791972261
 
< 0.1%
724481781
 
< 0.1%
723902521
 
< 0.1%
723536651
 
< 0.1%
723718341
 
< 0.1%
722037161
 
< 0.1%
722751981
 
< 0.1%
723536621
 
< 0.1%
721656101
 
< 0.1%
722678591
 
< 0.1%
Other values (5986015)5986015
> 99.9%
ValueCountFrequency (%)
99269011
< 0.1%
99269021
< 0.1%
99269031
< 0.1%
99269041
< 0.1%
99269931
< 0.1%
99269951
< 0.1%
99270841
< 0.1%
99270851
< 0.1%
99270861
< 0.1%
99297881
< 0.1%
ValueCountFrequency (%)
2987484821
< 0.1%
2987254831
< 0.1%
2987111761
< 0.1%
2987111731
< 0.1%
2987111711
< 0.1%
2987111701
< 0.1%
2987107451
< 0.1%
2987107411
< 0.1%
2987107361
< 0.1%
2987107211
< 0.1%
Distinct6940
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.7 MiB
Minimum2006-01-01 00:00:00
Maximum2024-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-15T14:41:53.594951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:53.624627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PD_CD
Real number (ℝ)

Distinct347
Distinct (%)< 0.1%
Missing884
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean496.35865
Minimum0
Maximum997
Zeros115
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size45.7 MiB
2025-10-15T14:41:53.653936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile101
Q1259
median503
Q3748
95-th percentile922
Maximum997
Range997
Interquartile range (IQR)489

Descriptive statistics

Standard deviation267.2434
Coefficient of variation (CV)0.53840786
Kurtosis-1.0788363
Mean496.35865
Median Absolute Deviation (MAD)244
Skewness0.02461015
Sum2.9707765 × 109
Variance71419.033
MonotonicityNot monotonic
2025-10-15T14:41:53.687341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101513024
 
8.6%
567423783
 
7.1%
478330145
 
5.5%
511314492
 
5.3%
339305917
 
5.1%
922232804
 
3.9%
849231321
 
3.9%
109229080
 
3.8%
397201538
 
3.4%
969178396
 
3.0%
Other values (337)3024641
50.5%
ValueCountFrequency (%)
0115
 
< 0.1%
136
 
< 0.1%
28
 
< 0.1%
411
 
< 0.1%
923
 
< 0.1%
1110
 
< 0.1%
1271
 
< 0.1%
15839
 
< 0.1%
165963
0.1%
29171
 
< 0.1%
ValueCountFrequency (%)
997171
 
< 0.1%
973247
 
< 0.1%
972178
 
< 0.1%
97021
 
< 0.1%
969178396
3.0%
9684890
 
0.1%
9674
 
< 0.1%
965303
 
< 0.1%
963180
 
< 0.1%
961564
 
< 0.1%

PD_DESC
Text

Distinct447
Distinct (%)< 0.1%
Missing9169
Missing (%)0.2%
Memory size435.8 MiB
2025-10-15T14:41:53.776186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length54
Median length41
Mean length27.407499
Min length6

Characters and Unicode

Total characters163810673
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)< 0.1%

Sample

1st rowSTRANGULATION 1ST
2nd rowSTRANGULATION 1ST
3rd rowRAPE 3
4th rowRAPE 1
5th row(null)
ValueCountFrequency (%)
3984469
 
5.3%
possession978149
 
5.3%
assault770334
 
4.1%
controlled611387
 
3.3%
4590854
 
3.2%
589565
 
3.2%
5519746
 
2.8%
marijuana513430
 
2.8%
from466541
 
2.5%
unclassified445571
 
2.4%
Other values (563)12154335
65.3%
2025-10-15T14:41:53.876301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S17804747
 
10.9%
A13294727
 
8.1%
E13276527
 
8.1%
13143956
 
8.0%
I12781461
 
7.8%
N11710503
 
7.1%
O8660835
 
5.3%
T8175873
 
5.0%
L8040767
 
4.9%
R7694778
 
4.7%
Other values (35)49226499
30.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)163810673
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S17804747
 
10.9%
A13294727
 
8.1%
E13276527
 
8.1%
13143956
 
8.0%
I12781461
 
7.8%
N11710503
 
7.1%
O8660835
 
5.3%
T8175873
 
5.0%
L8040767
 
4.9%
R7694778
 
4.7%
Other values (35)49226499
30.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)163810673
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S17804747
 
10.9%
A13294727
 
8.1%
E13276527
 
8.1%
13143956
 
8.0%
I12781461
 
7.8%
N11710503
 
7.1%
O8660835
 
5.3%
T8175873
 
5.0%
L8040767
 
4.9%
R7694778
 
4.7%
Other values (35)49226499
30.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)163810673
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S17804747
 
10.9%
A13294727
 
8.1%
E13276527
 
8.1%
13143956
 
8.0%
I12781461
 
7.8%
N11710503
 
7.1%
O8660835
 
5.3%
T8175873
 
5.0%
L8040767
 
4.9%
R7694778
 
4.7%
Other values (35)49226499
30.1%

KY_CD
Real number (ℝ)

High correlation 

Distinct76
Distinct (%)< 0.1%
Missing9788
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean292.87388
Minimum101
Maximum995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 MiB
2025-10-15T14:41:53.905174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile106
Q1121
median341
Q3348
95-th percentile677
Maximum995
Range894
Interquartile range (IQR)227

Descriptive statistics

Standard deviation177.80655
Coefficient of variation (CV)0.60710962
Kurtosis3.1701389
Mean292.87388
Median Absolute Deviation (MAD)106
Skewness1.5907753
Sum1.7502837 × 109
Variance31615.169
MonotonicityNot monotonic
2025-10-15T14:41:53.936270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
235827688
 
13.8%
344645227
 
10.8%
343332781
 
5.6%
117316371
 
5.3%
341306203
 
5.1%
106288434
 
4.8%
348243577
 
4.1%
677231742
 
3.9%
126222307
 
3.7%
352208608
 
3.5%
Other values (66)2353299
39.3%
ValueCountFrequency (%)
10121030
 
0.4%
102148
 
< 0.1%
103364
 
< 0.1%
10415291
 
0.3%
105202169
3.4%
106288434
4.8%
10796064
 
1.6%
109164147
2.7%
11023185
 
0.4%
11126035
 
0.4%
ValueCountFrequency (%)
99524804
 
0.4%
882171
 
< 0.1%
881183344
3.1%
8808479
 
0.1%
685175
 
< 0.1%
67817874
 
0.3%
677231742
3.9%
676519
 
< 0.1%
67514276
 
0.2%
672723
 
< 0.1%
Distinct90
Distinct (%)< 0.1%
Missing9169
Missing (%)0.2%
Memory size398.6 MiB
2025-10-15T14:41:54.008962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length43
Median length33
Mean length20.875512
Min length4

Characters and Unicode

Total characters124769927
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowFELONY ASSAULT
2nd rowFELONY ASSAULT
3rd rowRAPE
4th rowRAPE
5th row(null)
ValueCountFrequency (%)
offenses1450557
 
7.8%
dangerous1377233
 
7.4%
related1239365
 
6.6%
drugs1144070
 
6.1%
1131867
 
6.1%
assault933661
 
5.0%
other860155
 
4.6%
3656793
 
3.5%
laws579855
 
3.1%
larceny493535
 
2.6%
Other values (143)8814104
47.2%
2025-10-15T14:41:54.176755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E13024551
 
10.4%
12704339
 
10.2%
S11812500
 
9.5%
A10267769
 
8.2%
R8832815
 
7.1%
T7996095
 
6.4%
N7629493
 
6.1%
O7378636
 
5.9%
L6160459
 
4.9%
F5488855
 
4.4%
Other values (31)33474415
26.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)124769927
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E13024551
 
10.4%
12704339
 
10.2%
S11812500
 
9.5%
A10267769
 
8.2%
R8832815
 
7.1%
T7996095
 
6.4%
N7629493
 
6.1%
O7378636
 
5.9%
L6160459
 
4.9%
F5488855
 
4.4%
Other values (31)33474415
26.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)124769927
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E13024551
 
10.4%
12704339
 
10.2%
S11812500
 
9.5%
A10267769
 
8.2%
R8832815
 
7.1%
T7996095
 
6.4%
N7629493
 
6.1%
O7378636
 
5.9%
L6160459
 
4.9%
F5488855
 
4.4%
Other values (31)33474415
26.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)124769927
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E13024551
 
10.4%
12704339
 
10.2%
S11812500
 
9.5%
A10267769
 
8.2%
R8832815
 
7.1%
T7996095
 
6.4%
N7629493
 
6.1%
O7378636
 
5.9%
L6160459
 
4.9%
F5488855
 
4.4%
Other values (31)33474415
26.8%
Distinct2627
Distinct (%)< 0.1%
Missing196
Missing (%)< 0.1%
Memory size336.8 MiB
2025-10-15T14:41:54.245511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.999944
Min length2

Characters and Unicode

Total characters59857955
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique415 ?
Unique (%)< 0.1%

Sample

1st rowPL 1211200
2nd rowPL 1211300
3rd rowPL 1302503
4th rowPL 1303501
5th rowPL 2407800
ValueCountFrequency (%)
pl5037045
45.6%
1200001486407
 
4.4%
2211001406464
 
3.7%
1651503317125
 
2.9%
2200300314492
 
2.8%
1552500305917
 
2.8%
loc000000v223434
 
2.0%
vtl051101a165522
 
1.5%
1654000156770
 
1.4%
vtl0511001144075
 
1.3%
Other values (2622)3488064
31.6%
2025-10-15T14:41:54.337709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
016400705
27.4%
19267940
15.5%
L5904738
 
9.9%
25630682
 
9.4%
5059486
 
8.5%
P5046918
 
8.4%
54723422
 
7.9%
31502388
 
2.5%
61346612
 
2.2%
41208946
 
2.0%
Other values (31)3766118
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)59857955
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
016400705
27.4%
19267940
15.5%
L5904738
 
9.9%
25630682
 
9.4%
5059486
 
8.5%
P5046918
 
8.4%
54723422
 
7.9%
31502388
 
2.5%
61346612
 
2.2%
41208946
 
2.0%
Other values (31)3766118
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)59857955
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
016400705
27.4%
19267940
15.5%
L5904738
 
9.9%
25630682
 
9.4%
5059486
 
8.5%
P5046918
 
8.4%
54723422
 
7.9%
31502388
 
2.5%
61346612
 
2.2%
41208946
 
2.0%
Other values (31)3766118
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)59857955
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
016400705
27.4%
19267940
15.5%
L5904738
 
9.9%
25630682
 
9.4%
5059486
 
8.5%
P5046918
 
8.4%
54723422
 
7.9%
31502388
 
2.5%
61346612
 
2.2%
41208946
 
2.0%
Other values (31)3766118
 
6.3%

LAW_CAT_CD
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing24990
Missing (%)0.4%
Memory size285.6 MiB
M
3866398 
F
1767834 
V
 
297792
I
 
27200
9
 
1801

Length

Max length6
Median length1
Mean length1.0000084
Min length1

Characters and Unicode

Total characters5961085
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M3866398
64.6%
F1767834
29.5%
V297792
 
5.0%
I27200
 
0.5%
91801
 
< 0.1%
(null)10
 
< 0.1%
(Missing)24990
 
0.4%

Length

2025-10-15T14:41:54.362837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-15T14:41:54.384026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m3866398
64.9%
f1767834
29.7%
v297792
 
5.0%
i27200
 
0.5%
91801
 
< 0.1%
null10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
M3866398
64.9%
F1767834
29.7%
V297792
 
5.0%
I27200
 
0.5%
91801
 
< 0.1%
l20
 
< 0.1%
(10
 
< 0.1%
n10
 
< 0.1%
u10
 
< 0.1%
)10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)5961085
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M3866398
64.9%
F1767834
29.7%
V297792
 
5.0%
I27200
 
0.5%
91801
 
< 0.1%
l20
 
< 0.1%
(10
 
< 0.1%
n10
 
< 0.1%
u10
 
< 0.1%
)10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5961085
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M3866398
64.9%
F1767834
29.7%
V297792
 
5.0%
I27200
 
0.5%
91801
 
< 0.1%
l20
 
< 0.1%
(10
 
< 0.1%
n10
 
< 0.1%
u10
 
< 0.1%
)10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5961085
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M3866398
64.9%
F1767834
29.7%
V297792
 
5.0%
I27200
 
0.5%
91801
 
< 0.1%
l20
 
< 0.1%
(10
 
< 0.1%
n10
 
< 0.1%
u10
 
< 0.1%
)10
 
< 0.1%

ARREST_BORO
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing8
Missing (%)< 0.1%
Memory size285.4 MiB
K
1658691 
M
1592300 
B
1368787 
Q
1147992 
S
218247 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5986017
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowK
3rd rowK
4th rowB
5th rowQ

Common Values

ValueCountFrequency (%)
K1658691
27.7%
M1592300
26.6%
B1368787
22.9%
Q1147992
19.2%
S218247
 
3.6%
(Missing)8
 
< 0.1%

Length

2025-10-15T14:41:54.408715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-15T14:41:54.425167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
k1658691
27.7%
m1592300
26.6%
b1368787
22.9%
q1147992
19.2%
s218247
 
3.6%

Most occurring characters

ValueCountFrequency (%)
K1658691
27.7%
M1592300
26.6%
B1368787
22.9%
Q1147992
19.2%
S218247
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)5986017
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
K1658691
27.7%
M1592300
26.6%
B1368787
22.9%
Q1147992
19.2%
S218247
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5986017
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
K1658691
27.7%
M1592300
26.6%
B1368787
22.9%
Q1147992
19.2%
S218247
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5986017
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
K1658691
27.7%
M1592300
26.6%
B1368787
22.9%
Q1147992
19.2%
S218247
 
3.6%

ARREST_PRECINCT
Real number (ℝ)

High correlation 

Distinct80
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.039387
Minimum1
Maximum483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.7 MiB
2025-10-15T14:41:54.450093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q133
median60
Q388
95-th percentile115
Maximum483
Range482
Interquartile range (IQR)55

Descriptive statistics

Standard deviation34.419945
Coefficient of variation (CV)0.56389729
Kurtosis-1.1129989
Mean61.039387
Median Absolute Deviation (MAD)27
Skewness0.13929039
Sum3.653833 × 108
Variance1184.7326
MonotonicityNot monotonic
2025-10-15T14:41:54.478748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14208437
 
3.5%
75201899
 
3.4%
44190208
 
3.2%
40177271
 
3.0%
73158637
 
2.7%
46148354
 
2.5%
43144668
 
2.4%
52140123
 
2.3%
25129453
 
2.2%
103128643
 
2.1%
Other values (70)4358332
72.8%
ValueCountFrequency (%)
172317
 
1.2%
590035
1.5%
664254
 
1.1%
754715
 
0.9%
961588
 
1.0%
1050372
 
0.8%
1378123
 
1.3%
14208437
3.5%
1730224
 
0.5%
1886402
1.4%
ValueCountFrequency (%)
4833
 
< 0.1%
12323981
 
0.4%
12249903
0.8%
12134453
 
0.6%
120109910
1.8%
116111
 
< 0.1%
115110800
1.9%
11497063
1.6%
113116850
2.0%
11239525
 
0.7%

JURISDICTION_CODE
Real number (ℝ)

Zeros 

Distinct30
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.2534464
Minimum0
Maximum97
Zeros5028649
Zeros (%)84.0%
Negative0
Negative (%)0.0%
Memory size45.7 MiB
2025-10-15T14:41:54.503758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum97
Range97
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.2122302
Coefficient of variation (CV)7.3495206
Kurtosis85.396658
Mean1.2534464
Median Absolute Deviation (MAD)0
Skewness9.2161239
Sum7503149
Variance84.865185
MonotonicityNot monotonic
2025-10-15T14:41:54.530394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
05028649
84.0%
1501880
 
8.4%
2298656
 
5.0%
350611
 
0.8%
9734071
 
0.6%
7216985
 
0.3%
411610
 
0.2%
737690
 
0.1%
696908
 
0.1%
65503
 
0.1%
Other values (20)23452
 
0.4%
ValueCountFrequency (%)
05028649
84.0%
1501880
 
8.4%
2298656
 
5.0%
350611
 
0.8%
411610
 
0.2%
65503
 
0.1%
73635
 
0.1%
812
 
< 0.1%
9573
 
< 0.1%
114308
 
0.1%
ValueCountFrequency (%)
9734071
0.6%
88281
 
< 0.1%
871022
 
< 0.1%
85650
 
< 0.1%
822
 
< 0.1%
79212
 
< 0.1%
7652
 
< 0.1%
74130
 
< 0.1%
737690
 
0.1%
7216985
0.3%
Distinct91
Distinct (%)< 0.1%
Missing17
Missing (%)< 0.1%
Memory size307.3 MiB
2025-10-15T14:41:54.579525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.8312054
Min length3

Characters and Unicode

Total characters28919634
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)< 0.1%

Sample

1st row25-44
2nd row25-44
3rd row25-44
4th row45-64
5th row<18
ValueCountFrequency (%)
25-442874418
48.0%
18-241491336
24.9%
45-641115032
 
18.6%
18447699
 
7.5%
6557345
 
1.0%
89513
 
< 0.1%
9457
 
< 0.1%
8947
 
< 0.1%
9357
 
< 0.1%
9285
 
< 0.1%
Other values (81)139
 
< 0.1%
2025-10-15T14:41:54.650933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49470294
32.7%
-5480786
19.0%
24365818
15.1%
54046839
14.0%
81939082
 
6.7%
11939078
 
6.7%
61172394
 
4.1%
<447699
 
1.5%
+57345
 
0.2%
9156
 
< 0.1%
Other values (8)143
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)28919634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
49470294
32.7%
-5480786
19.0%
24365818
15.1%
54046839
14.0%
81939082
 
6.7%
11939078
 
6.7%
61172394
 
4.1%
<447699
 
1.5%
+57345
 
0.2%
9156
 
< 0.1%
Other values (8)143
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)28919634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
49470294
32.7%
-5480786
19.0%
24365818
15.1%
54046839
14.0%
81939082
 
6.7%
11939078
 
6.7%
61172394
 
4.1%
<447699
 
1.5%
+57345
 
0.2%
9156
 
< 0.1%
Other values (8)143
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)28919634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
49470294
32.7%
-5480786
19.0%
24365818
15.1%
54046839
14.0%
81939082
 
6.7%
11939078
 
6.7%
61172394
 
4.1%
<447699
 
1.5%
+57345
 
0.2%
9156
 
< 0.1%
Other values (8)143
 
< 0.1%

PERP_SEX
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size285.4 MiB
M
4971527 
F
1010994 
U
 
3504

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5986025
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M4971527
83.1%
F1010994
 
16.9%
U3504
 
0.1%

Length

2025-10-15T14:41:54.672744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-15T14:41:54.686972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m4971527
83.1%
f1010994
 
16.9%
u3504
 
0.1%

Most occurring characters

ValueCountFrequency (%)
M4971527
83.1%
F1010994
 
16.9%
U3504
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)5986025
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M4971527
83.1%
F1010994
 
16.9%
U3504
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5986025
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M4971527
83.1%
F1010994
 
16.9%
U3504
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5986025
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M4971527
83.1%
F1010994
 
16.9%
U3504
 
0.1%

PERP_RACE
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size331.0 MiB
BLACK
2904570 
WHITE HISPANIC
1551099 
WHITE
705233 
BLACK HISPANIC
495249 
ASIAN / PACIFIC ISLANDER
 
258657
Other values (3)
 
71217

Length

Max length30
Median length5
Mean length8.9744737
Min length5

Characters and Unicode

Total characters53721424
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWHITE
2nd rowBLACK
3rd rowBLACK
4th rowBLACK
5th rowWHITE HISPANIC

Common Values

ValueCountFrequency (%)
BLACK2904570
48.5%
WHITE HISPANIC1551099
25.9%
WHITE705233
 
11.8%
BLACK HISPANIC495249
 
8.3%
ASIAN / PACIFIC ISLANDER258657
 
4.3%
UNKNOWN55942
 
0.9%
AMERICAN INDIAN/ALASKAN NATIVE13912
 
0.2%
OTHER1363
 
< 0.1%

Length

2025-10-15T14:41:54.706427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-15T14:41:54.728144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
black3399819
38.5%
white2256332
25.5%
hispanic2046348
23.2%
asian258657
 
2.9%
258657
 
2.9%
pacific258657
 
2.9%
islander258657
 
2.9%
unknown55942
 
0.6%
american13912
 
0.2%
indian/alaskan13912
 
0.2%
Other values (2)15275
 
0.2%

Most occurring characters

ValueCountFrequency (%)
I7439304
13.8%
A6578179
12.2%
C5977393
11.1%
H4304043
8.0%
L3672388
 
6.8%
K3469673
 
6.5%
B3399819
 
6.3%
2850143
 
5.3%
N2801048
 
5.2%
S2577574
 
4.8%
Other values (12)10651860
19.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)53721424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I7439304
13.8%
A6578179
12.2%
C5977393
11.1%
H4304043
8.0%
L3672388
 
6.8%
K3469673
 
6.5%
B3399819
 
6.3%
2850143
 
5.3%
N2801048
 
5.2%
S2577574
 
4.8%
Other values (12)10651860
19.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)53721424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I7439304
13.8%
A6578179
12.2%
C5977393
11.1%
H4304043
8.0%
L3672388
 
6.8%
K3469673
 
6.5%
B3399819
 
6.3%
2850143
 
5.3%
N2801048
 
5.2%
S2577574
 
4.8%
Other values (12)10651860
19.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)53721424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I7439304
13.8%
A6578179
12.2%
C5977393
11.1%
H4304043
8.0%
L3672388
 
6.8%
K3469673
 
6.5%
B3399819
 
6.3%
2850143
 
5.3%
N2801048
 
5.2%
S2577574
 
4.8%
Other values (12)10651860
19.8%

X_COORD_CD
Real number (ℝ)

High correlation 

Distinct73438
Distinct (%)1.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1005375.2
Minimum0
Maximum1067302
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size45.7 MiB
2025-10-15T14:41:54.759274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile981343
Q1992932
median1004937
Q31016155
95-th percentile1041879
Maximum1067302
Range1067302
Interquartile range (IQR)23223

Descriptive statistics

Standard deviation20268.751
Coefficient of variation (CV)0.020160386
Kurtosis12.74163
Mean1005375.2
Median Absolute Deviation (MAD)11564
Skewness-0.42544777
Sum6.0181998 × 1012
Variance4.1082229 × 108
MonotonicityNot monotonic
2025-10-15T14:41:54.788180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101711924586
 
0.4%
98722024499
 
0.4%
100653719960
 
0.3%
102648618173
 
0.3%
102018317918
 
0.3%
96282217829
 
0.3%
99789717616
 
0.3%
98707816124
 
0.3%
100504116053
 
0.3%
100769415826
 
0.3%
Other values (73428)5797440
96.8%
ValueCountFrequency (%)
011
< 0.1%
9133573
 
< 0.1%
9134111
 
< 0.1%
91346310
< 0.1%
9135128
< 0.1%
9135542
 
< 0.1%
9136261
 
< 0.1%
9136821
 
< 0.1%
9138183
 
< 0.1%
9138442
 
< 0.1%
ValueCountFrequency (%)
10673021
 
< 0.1%
10672981
 
< 0.1%
10672495
 
< 0.1%
106722610
 
< 0.1%
10672202
 
< 0.1%
106718525
< 0.1%
10671511
 
< 0.1%
10671174
 
< 0.1%
10671136
 
< 0.1%
10670534
 
< 0.1%

Y_COORD_CD
Real number (ℝ)

High correlation  Skewed 

Distinct77889
Distinct (%)1.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean213866.8
Minimum0
Maximum8202360
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size45.7 MiB
2025-10-15T14:41:54.816207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile159815
Q1186782
median208981
Q3236608
95-th percentile254156
Maximum8202360
Range8202360
Interquartile range (IQR)49826

Descriptive statistics

Standard deviation151374.39
Coefficient of variation (CV)0.70779751
Kurtosis1523.2051
Mean213866.8
Median Absolute Deviation (MAD)24819
Skewness36.551557
Sum1.2802118 × 1012
Variance2.2914205 × 1010
MonotonicityNot monotonic
2025-10-15T14:41:54.846270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18390924547
 
0.4%
21267624537
 
0.4%
23453320176
 
0.3%
24451119927
 
0.3%
26259118096
 
0.3%
17428217821
 
0.3%
21515716124
 
0.3%
18378915583
 
0.3%
21695415281
 
0.3%
23928315111
 
0.3%
Other values (77879)5798821
96.9%
ValueCountFrequency (%)
011
< 0.1%
1211313
 
< 0.1%
1211525
< 0.1%
1211742
 
< 0.1%
1212193
 
< 0.1%
1212505
< 0.1%
1212822
 
< 0.1%
1213123
 
< 0.1%
1213433
 
< 0.1%
1213902
 
< 0.1%
ValueCountFrequency (%)
8202360155
 
< 0.1%
8187668157
 
< 0.1%
7250292144
 
< 0.1%
723618732
 
< 0.1%
7220451225
< 0.1%
7209909295
< 0.1%
71920442
 
< 0.1%
7186840167
 
< 0.1%
6253476107
 
< 0.1%
6216843432
< 0.1%

Latitude
Real number (ℝ)

High correlation  Skewed 

Distinct186539
Distinct (%)3.1%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean40.753426
Minimum0
Maximum62.083075
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size45.7 MiB
2025-10-15T14:41:54.872870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.605247
Q140.679284
median40.740236
Q340.816088
95-th percentile40.864235
Maximum62.083075
Range62.083075
Interquartile range (IQR)0.13680326

Descriptive statistics

Standard deviation0.41295175
Coefficient of variation (CV)0.010132934
Kurtosis1615.1641
Mean40.753426
Median Absolute Deviation (MAD)0.068142762
Skewness33.478516
Sum2.4395082 × 108
Variance0.17052915
MonotonicityNot monotonic
2025-10-15T14:41:54.900937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.7504307719911
 
0.3%
40.6714116619850
 
0.3%
40.6450227517821
 
0.3%
40.8103984916047
 
0.3%
40.8377816215797
 
0.3%
40.7572405314915
 
0.2%
40.6800487314517
 
0.2%
40.6488671314429
 
0.2%
40.8873328214334
 
0.2%
40.8233872914304
 
0.2%
Other values (186529)5824095
97.3%
ValueCountFrequency (%)
011
< 0.1%
40.498905363
 
< 0.1%
40.498957015
< 0.1%
40.499025362
 
< 0.1%
40.499142793
 
< 0.1%
40.499228795
< 0.1%
40.49932362
 
< 0.1%
40.4993931
 
< 0.1%
40.499400832
 
< 0.1%
40.499486863
 
< 0.1%
ValueCountFrequency (%)
62.08307498155
 
< 0.1%
62.0459844157
 
< 0.1%
59.65727395144
 
< 0.1%
59.6209612232
 
< 0.1%
59.58050882225
< 0.1%
59.55331498295
< 0.1%
59.507372632
 
< 0.1%
59.49372016167
 
< 0.1%
57.07018725107
 
< 0.1%
56.97414271432
< 0.1%

Longitude
Real number (ℝ)

High correlation  Skewed 

Distinct188004
Distinct (%)3.1%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-73.923571
Minimum-74.254939
Maximum0
Zeros11
Zeros (%)< 0.1%
Negative5986009
Negative (%)> 99.9%
Memory size45.7 MiB
2025-10-15T14:41:54.928474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-74.254939
5-th percentile-74.010474
Q1-73.968669
median-73.925311
Q3-73.884691
95-th percentile-73.792139
Maximum0
Range74.254939
Interquartile range (IQR)0.083977468

Descriptive statistics

Standard deviation0.12396636
Coefficient of variation (CV)-0.001676953
Kurtosis232362.93
Mean-73.923571
Median Absolute Deviation (MAD)0.041741562
Skewness389.6237
Sum-4.4250798 × 108
Variance0.015367658
MonotonicityNot monotonic
2025-10-15T14:41:54.959866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-73.9892821819911
 
0.3%
-73.8815117219850
 
0.3%
-73.9194579716955
 
0.3%
-74.0772168516618
 
0.3%
-73.9897936414915
 
0.2%
-73.8799983114828
 
0.2%
-73.9248953114694
 
0.2%
-73.7759091914517
 
0.2%
-73.950821914429
 
0.2%
-73.8472500114334
 
0.2%
Other values (187994)5824969
97.3%
ValueCountFrequency (%)
-74.254938743
 
< 0.1%
-74.254743191
 
< 0.1%
-74.2545598110
< 0.1%
-74.2543778
< 0.1%
-74.254222952
 
< 0.1%
-74.253951131
 
< 0.1%
-74.253767031
 
< 0.1%
-74.2532563
 
< 0.1%
-74.253187243
 
< 0.1%
-74.2531871
 
< 0.1%
ValueCountFrequency (%)
011
 
< 0.1%
-73.68178027169
< 0.1%
-73.68478838167
< 0.1%
-73.700293351
 
< 0.1%
-73.700315861
 
< 0.1%
-73.700493395
 
< 0.1%
-73.700567864
 
< 0.1%
-73.700576516
 
< 0.1%
-73.700596852
 
< 0.1%
-73.7007176
 
< 0.1%

Lon_Lat
Text

Distinct198169
Distinct (%)3.3%
Missing5
Missing (%)< 0.1%
Memory size523.1 MiB
2025-10-15T14:41:55.097368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length45
Median length44
Mean length42.630321
Min length11

Characters and Unicode

Total characters255185954
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47756 ?
Unique (%)0.8%

Sample

1st rowPOINT (-73.985702 40.76539)
2nd rowPOINT (-73.95082 40.648859)
3rd rowPOINT (-73.9305713255961 40.6744956865259)
4th rowPOINT (-73.9005768807295 40.8535983673823)
5th rowPOINT (-73.901881 40.699373)
ValueCountFrequency (%)
point5986020
33.3%
73.9892821759999620875
 
0.1%
40.7504307680000519911
 
0.1%
40.6714116630000719850
 
0.1%
73.8815117239999519850
 
0.1%
73.9194579709999916955
 
0.1%
74.07721684716618
 
0.1%
40.64502274600004516618
 
0.1%
73.9248953109999416047
 
0.1%
40.81039849400002616047
 
0.1%
Other values (374460)11809269
65.8%
2025-10-15T14:41:55.257401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
037180848
14.6%
933532857
13.1%
719781514
 
7.8%
418361995
 
7.2%
316356123
 
6.4%
814300140
 
5.6%
612923052
 
5.1%
11972040
 
4.7%
.11972018
 
4.7%
511890639
 
4.7%
Other values (10)66914728
26.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)255185954
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
037180848
14.6%
933532857
13.1%
719781514
 
7.8%
418361995
 
7.2%
316356123
 
6.4%
814300140
 
5.6%
612923052
 
5.1%
11972040
 
4.7%
.11972018
 
4.7%
511890639
 
4.7%
Other values (10)66914728
26.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)255185954
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
037180848
14.6%
933532857
13.1%
719781514
 
7.8%
418361995
 
7.2%
316356123
 
6.4%
814300140
 
5.6%
612923052
 
5.1%
11972040
 
4.7%
.11972018
 
4.7%
511890639
 
4.7%
Other values (10)66914728
26.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)255185954
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
037180848
14.6%
933532857
13.1%
719781514
 
7.8%
418361995
 
7.2%
316356123
 
6.4%
814300140
 
5.6%
612923052
 
5.1%
11972040
 
4.7%
.11972018
 
4.7%
511890639
 
4.7%
Other values (10)66914728
26.2%

Interactions

2025-10-15T14:41:37.683689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:18.350626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:20.817035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:23.335525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:25.716188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:28.268123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:30.609917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:32.939461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:35.257400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:37.932423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:18.621243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:21.094429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:23.595136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:26.001271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:28.576015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:30.861675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:33.205563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:35.530507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:38.169795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:18.886477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:21.381526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:23.844210image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:26.285841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:28.829539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:31.116027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:33.445123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:35.810493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:38.418738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:19.142305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:21.685219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:24.116159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:26.536114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:29.098421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:31.387736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:33.736444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:36.112273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:38.659057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:19.420459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:21.955674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:24.361421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:26.814412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:29.333466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:31.646736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:33.982850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:36.370721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:38.891990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:19.702159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:22.223969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:24.619335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:27.105679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:29.583558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:31.890311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:34.232988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:36.652584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:39.137090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:19.971691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:22.499324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:24.896568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:27.406531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:29.834820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:32.148797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:34.502331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:36.905591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:39.378047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:20.245287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:22.771517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:25.150253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:27.697962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:30.077740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:32.407426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:34.747911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:37.144213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:39.611618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:20.515197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:23.051951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:25.393087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:27.993145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:30.334090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:32.659831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:34.989154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-15T14:41:37.388030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-15T14:41:55.281255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ARREST_BOROARREST_KEYARREST_PRECINCTJURISDICTION_CODEKY_CDLAW_CAT_CDLatitudeLongitudePD_CDPERP_RACEPERP_SEXX_COORD_CDY_COORD_CD
ARREST_BORO1.0000.0220.7870.0320.0720.0660.0210.0010.0980.1700.0150.5730.023
ARREST_KEY0.0221.0000.025-0.041-0.0990.090-0.030-0.001-0.1370.0260.060-0.001-0.030
ARREST_PRECINCT0.7870.0251.000-0.116-0.0200.047-0.4750.4060.0130.1420.0050.407-0.475
JURISDICTION_CODE0.032-0.041-0.1161.0000.1490.0190.036-0.0560.0370.0210.013-0.0560.036
KY_CD0.072-0.099-0.0200.1491.0000.7240.013-0.0060.2890.0300.063-0.0060.013
LAW_CAT_CD0.0660.0900.0470.0190.7241.0000.0130.0210.4070.0270.0500.0280.007
Latitude0.021-0.030-0.4750.0360.0130.0131.0000.266-0.0310.0050.0040.2641.000
Longitude0.001-0.0010.406-0.056-0.0060.0210.2661.000-0.0010.0020.0001.0000.266
PD_CD0.098-0.1370.0130.0370.2890.407-0.031-0.0011.0000.0410.100-0.001-0.031
PERP_RACE0.1700.0260.1420.0210.0300.0270.0050.0020.0411.0000.0390.1110.005
PERP_SEX0.0150.0600.0050.0130.0630.0500.0040.0000.1000.0391.0000.0130.004
X_COORD_CD0.573-0.0010.407-0.056-0.0060.0280.2641.000-0.0010.1110.0131.0000.265
Y_COORD_CD0.023-0.030-0.4750.0360.0130.0071.0000.266-0.0310.0050.0040.2651.000

Missing values

2025-10-15T14:41:40.494724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-15T14:41:44.290171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-15T14:41:51.063418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ARREST_KEYARREST_DATEPD_CDPD_DESCKY_CDOFNS_DESCLAW_CODELAW_CAT_CDARREST_BOROARREST_PRECINCTJURISDICTION_CODEAGE_GROUPPERP_SEXPERP_RACEX_COORD_CDY_COORD_CDLatitudeLongitudeLon_Lat
027919722612/19/2023105.0STRANGULATION 1ST106.0FELONY ASSAULTPL 1211200FM180.025-44MWHITE988210.0218129.040.765390-73.985702POINT (-73.985702 40.76539)
127876184012/09/2023105.0STRANGULATION 1ST106.0FELONY ASSAULTPL 1211300FK670.025-44MBLACK997897.0175676.040.648859-73.950820POINT (-73.95082 40.648859)
227850676112/05/2023153.0RAPE 3104.0RAPEPL 1302503FK770.025-44MBLACK1003509.0185018.040.674496-73.930571POINT (-73.9305713255961 40.6744956865259)
327843640812/03/2023157.0RAPE 1104.0RAPEPL 1303501FB460.045-64MBLACK1011755.0250279.040.853598-73.900577POINT (-73.9005768807295 40.8535983673823)
427824875311/29/2023660.0(null)NaN(null)PL 2407800MQ1040.0<18MWHITE HISPANIC1011456.0194092.040.699373-73.901881POINT (-73.901881 40.699373)
527825459311/29/2023464.0JOSTLING230.0JOSTLINGPL 1652501MM180.0<18MWHITE HISPANIC990503.0215519.040.758225-73.977428POINT (-73.977428 40.758225)
627785080711/21/2023263.0ARSON 2,3,4114.0ARSONPL 1501001FK6371.025-44MWHITE1000734.0164367.040.617813-73.940621POINT (-73.940621 40.617813)
727652358210/26/2023177.0SEXUAL ABUSE116.0SEX CRIMESPL 2603204FM280.025-44MBLACK997407.0233806.040.808418-73.952474POINT (-73.9524740603515 40.8084177460021)
827646650510/25/2023157.0RAPE 1104.0RAPEPL 1303501FK770.025-44MBLACK1003509.0185018.040.674496-73.930571POINT (-73.9305713255961 40.6744956865259)
927639149410/24/2023168.0SODOMY 1116.0SEX CRIMESPL 1305004FK770.045-64MWHITE1003509.0185018.040.674496-73.930571POINT (-73.9305713255961 40.6744956865259)
ARREST_KEYARREST_DATEPD_CDPD_DESCKY_CDOFNS_DESCLAW_CODELAW_CAT_CDARREST_BOROARREST_PRECINCTJURISDICTION_CODEAGE_GROUPPERP_SEXPERP_RACEX_COORD_CDY_COORD_CDLatitudeLongitudeLon_Lat
598601529761112612/06/2024244.0BURGLARY,UNCLASSIFIED,UNKNOWN107.0BURGLARYPL 1402501FM130.045-64FBLACK985689.0208933.040.740159-73.994807POINT (-73.994807 40.740159)
598601629753647612/05/2024478.0THEFT OF SERVICES, UNCLASSIFIE343.0OTHER OFFENSES RELATED TO THEFTPL 1651503MB521.025-44MBLACK1013463.0254828.040.866070-73.894382POINT (-73.89438159169373 40.866070221647036)
598601729644294011/13/2024759.0PUBLIC ADMINISTATION,UNCLASS M359.0OFFENSES AGAINST PUBLIC ADMINIPL 1950500MQ1010.025-44MWHITE1051297.0160407.040.606704-73.758533POINT (-73.758533 40.606704)
598601829726676911/30/2024439.0LARCENY,GRAND FROM OPEN AREAS, UNATTENDED109.0GRAND LARCENYPL 1553501FB430.018-24MWHITE HISPANIC1021611.0245695.040.840972-73.864972POINT (-73.864972 40.840972)
598601929866179112/30/2024113.0MENACING,UNCLASSIFIED344.0ASSAULT 3 & RELATED OFFENSESPL 1201401MB430.025-44MWHITE HISPANIC1017513.0240674.040.827216-73.879808POINT (-73.879808 40.827216)
598602029747003712/04/2024478.0THEFT OF SERVICES, UNCLASSIFIE343.0OTHER OFFENSES RELATED TO THEFTPL 1651503MB461.025-44MWHITE HISPANIC1010256.0248770.040.849453-73.906000POINT (-73.90599986315169 40.84945286686186)
598602129819642412/19/2024244.0BURGLARY,UNCLASSIFIED,UNKNOWN107.0BURGLARYPL 1402000FM60.018-24MBLACK983555.0204888.040.729056-74.002507POINT (-74.002507 40.729056)
598602229849990612/26/2024101.0ASSAULT 3344.0ASSAULT 3 & RELATED OFFENSESPL 1200001MK830.045-64FBLACK1007005.0195927.040.704433-73.917928POINT (-73.917928 40.704433)
598602329749513712/04/2024101.0ASSAULT 3344.0ASSAULT 3 & RELATED OFFENSESPL 1200001MM140.025-44MASIAN / PACIFIC ISLANDER986732.0211747.040.747873-73.991040POINT (-73.99104 40.747873)
598602429854026512/27/2024639.0AGGRAVATED HARASSMENT 2361.0OFF. AGNST PUB ORD SENSBLTY &PL 2403001MK660.025-44FWHITE986735.0167242.040.625726-73.991049POINT (-73.991049 40.625726)